Risk Management Determinants Affecting Firms' Values in the Gold Mining Industry: New Empirical Results
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Risk Management Determinants Affecting Firms' Values
in the Gold Mining Industry: New Empirical Results
by Georges Dionne* and Martin Garand
Risk Management Chair, HEC–Montreal
* Corresponding author:
3000, Chemin de la Cote-Sainte-Catherine, Montreal, Quebec Canada, H3T 2A7
phone 514-340-6596, fax 514-340-5019, georges.dionne@hec.ca
The authors would like to thank Mathieu Maurice (from the Université de Montréal’s Center for Research in
Transportation) for his help in estimating the econometric models; Ted Reeve and Martin Boyer for their
collaboration in obtaining certain data; and participants in the two conferences on integrated risk management
in non-financial firms (held at HEC-Montreal, 14 April 2000, and at FFSA, Paris, 26 May 2000) and in
conferences at Université de Strasbourg, Université d'Orléans and Université Laval for their comments. The data
can be obtained by writing to the corresponding author.
Abstract
The goal of this article is to isolate the significant determinants that affect the decision of non-financial
firms to hedge their risks. Our application is for the North American gold mining industry. The random
variable considered is the selling price of an ounce of gold. We show that several factors related to
maximizing the firm's value significantly affect the decision to hedge the price of gold.
Keywords: Risk management, non-financial companies, Tobit, panel, tax-save.
JEL Classification: D80, G10.
1. INTRODUCTION
For more than 15 years, the finance literature has featured discussions about the theoretical
determinants of risk management, but very little has been done to measure effectively the relevance of
the various determinants proposed. Tufano (1996) identifies two classes of arguments used to show
why non-financial firms might undertake risk-management activities: to maximize the firm’s value and
to protect risk-averse managers. These two classes of argument were developed in the principal
theoretical studies on the subject (see the recent contributions of Stulz, 1996, Doherty, 2000, Froot,
Scharfstein and Stein, 1993, Caillaud, Dionne and Julien, 2000, MacMinn and Gaven, 2000). The two
main results of Tufano’s study are that whereas the determinants proposed to maximize the firm’s value
have practically no significance; the determinants related to managers’ risk behavior do.
The goal of this article is to determine what theoretical determinants are statistically significant in
explaining how tightly non-financial firms manage risks, as applied to the North American mining
industry. In a recent review article, J. Cliche (2000) concluded that the statistical data and methods used
to measure the relevance of different determinants do not always stand up to the authors’ ambitions. It
is, therefore, quite likely that the absence of significant findings in the literature on effects related to
1maximization of the firm’s value may be better explained by inadequacies in the empirical studies
published to date rather than by weaknesses in the theories advocating that firms take an integrated risk-
management approach. We share these conclusions. It is very difficult to obtain relevant data and to
construct samples large enough to isolate statistically significant effects. The article published by
Tufano (1996) offered a first attempt to improve the methodology. In this article, we propose to extend
his analysis. We, consequently, updated the author’s data base and recalculated the determinants
affecting the firm’s value. We employed a method that take explicitly into account the time-sensitive
(or panel) aspect of the data and we applied the method to quarterly data in order to better fit the
continuous decision process of the managers. We also used the methodology proposed by Graham and
Smith (1999) to measure the effect of the tax incentive on hedging. After reviewing the principal
determinants influencing risk management decisions in non-financial firms, we give a brief description
of our methodology and discuss our results. In closing, we sum up the main points of our study.
2. WHAT ARE THE MAIN DETERMINANTS AND HOW CAN THEY BE MEASURED?
Let us review the main theoretical determinants proposed to maximize the firm’s value. We shall not
give a detailed justification of each of the determinants, as they are well documented in the articles
discussed. We choose rather to identify them and show how they can be measured to produce statistical
findings. A more formal description of the variables used in the empirical analysis is given in the
Appendix.
Leading into the nub of the matter, we will immediately point out that, in the companies studied, the
main risk resides in the selling price of an ounce of gold. In other words, our study focuses on the way
risks are managed by executives of mining companies whose main production is gold. Four principal
determinants are often cited in the literature to justify risk-management activities: 1) reducing the
expected costs of financial distress; 2) reducing the risk premiums payable to various partners;
3) increasing investment possibilities; 4) reducing expected tax payments. As already mentioned, the
articles by Stulz (1996) and by Froot et al. (1993) take a detailed look at the motivations behind these
determinants. We here intend to show how those theoretical effects can be measured statistically.
Reducing the costs of financial distress and risk premiums to partners: These costs will be high
mainly for firms with heavy debts. In the empirical studies consulted, these first two determinants are
often grouped together, because there are no variables capable of distinguishing between them. The
intuition of a positive effect on the firm’s level of hedging resides in the following: firms with high
expected financial-distress costs and those with high risk premiums to pay are more strongly motivated
to hedge their risks in order to reduce these costs and increase the firm’s net value.
Four variables are used to measure such costs. The average direct and indirect monetary production or
operating costs (cash-costs) can be used based on the argument that firms with high production costs
are less efficient; more prone to financial failure; and pay higher premiums to their partners. Interest
costs and non-monetary costs, such as mortgages, are not included. The cost of producing an ounce of
gold represents the mining company’s short-term technology. If the price of gold falls below the
average cost of production, the firm will find itself in financial difficulty, a more likely eventuality for
inefficient firms.
2Long-term debt weighted according to market value is the second variable used to measure the costs of
financial distress. The previous variable is more closely related to the probability of financial distress,
whereas this one has more to do with the costs resulting from financial distress, supposing that such
costs are proportional to the face value of debt. We can thus predict a positive sign for these two
variables in the case of firms whose capital structure calls for protection, meaning firms that use a lot of
external financing. We also consider payment of dividends and the use of preferred shares to measure
financing possibilities other than debt. A negative sign is predicted for these two variables.
Increasing investment possibilities: The leading argument here hinges on the reduction of fluctuations
in internal earnings by hedging risks so as to minimize the need to seek external financing for
investment projects, especially in the event of low internal earnings. It is well known that external
financing is more costly because investors and entrepreneurs are in a situation of asymmetrical
information regarding the quality of investment projects (Froot, Scharfstein, and Stein, 1993).
Thus, if we have a single random variable such as internal earnings, we should observe a positive
relationship between investment opportunities and the hedging of risks. But this relationship can be
very weak and even negative if the values of the investment opportunities are themselves random. To
be specific, if the latter are also linked to the price of gold, natural diversification will occur within the
firm and there will be much less need for hedging. For example, if the price of gold has a positive effect
on both investment opportunities and the source of internal financing, then the two values will be
positively correlated: they will be high when the firm needs to invest and low when there is no need to
invest. Two variables can be used to measure investment opportunities: exploration and acquisitions.
Their predictive signs on risk management are undetermined or even negative under natural
diversification. They will be positive otherwise. As for acquisition activities, we must distinguish
between those within the industry and those external to the industry, the latter being interpreted as a
substitute for the diversification of risks (Tufano, 1996).
The convex nature of taxes: With a convex tax structure, hedging to cover risks may cut average taxes
by reducing fluctuations in earnings. The simplest argument is linked to governments’ asymmetrical
methods of taxation, which creates the desired convexity (see, for example, the discussion in Doherty,
2000 and Graham and Smith, 1999). But accounting for tax incentives is very complex and it is not
obvious that the variables used by researchers are always the appropriate ones for measuring the
convexity of the tax structure, which may explain the counter-intuitive empirical results sometimes
obtained in the literature.
For Graham and Smith (1999), the different variables proposed to measure the convexity of taxes are
not really suitable to this function. The empirical relevance of studies analyzing empirical relevance are
often limited, because their statistical strength is undermined by the small number of observations
available. Their data show that tax functions are convex for only about 50% of the firms in their
sample, whereas all researchers model convexity in the same way for all the firms in their sample. They
have thus used different simulations and formulated an equation allowing other researchers to compute
the taxes saved as a result of risk management. This computed tax save variable should have a positive
effect on hedging. We applied their model to compute a tax save variable. We also used a different tax
variable. This variable (deferred income tax) measures the inverse of the tax function's convexity. So a
negative sign is predicted. A positive sign is predicted for the tax save variable derived from the
Graham and Smith model.
3Size of the firm: This control variable can be used as an indirect measure of financing costs. As a rule,
the largest firms have greater negotiation power and thus lower average financing costs, which reduces
the need to hedge risks. But this variable can also measure the accessibility of different methods of
diversifying risks: qualified personnel, software, consulting fees. Most surveys show that it is the larger
firms which tend to diversify, whereas it is the smaller ones that are in greatest need of such a strategy
(Stulz, 1996). We should thus expect this variable to have an ambiguous sign.
Liquidity ratio: This variable measures the liquidity reserve for emergencies. A negative sign is
predicted. It is worth noting that the risk management of firms’ liquidity is an important risk-
management activity.
Financial constraint: Finally, we may consider a financial-constraint variable as a substitute to both
long-term debt and the liquidity ratio. A positive sign is predicted since firms with high financial
constraint may use hedging to reduce the burden of that constraint.
3. CONSTRUCTION OF THE DEPENDENT VARIABLE
The dependent variable measures the percentage of gold production covered by risk-management
activities. This is the Delta %, the fraction of gold production (at date t) whose price is covered for the
t
remainder of the current year and the next two years. Among the main transactions, we find, for
example: forward sales of quantities of gold at a price fixed over a number of years; gold loans with
reimbursements in ounces of gold over a number of years; holding of put options; and selling of call
options on the market. These different quantities of ounces of gold whose price is covered by various
sorts of contracts can be transformed into Delta ounces by using the financial theory of derivatives. The
Delta is the slope of the curve linking the value of an underlying asset to that of a derivative product
such as an option, but it may also be interpreted as the quantity in the underlying asset (short or long)
needed to construct the replicating portfolio.
For loans payable in ounces of gold and for forward-sales contracts, the Delta is equal to -1, because
there is no uncertainty that the transaction will occur; but, for options, Delta values must be computed
using the Black and Scholes formula, which takes into account the probability that the option will be
exercised. Finally, we can compute the firm’s total Delta % by dividing the sum of ounces whose price
is covered over the same period by the total estimated production over the remainder of the current year
and the subsequent two calendar years. Table 1 shows the distribution of the Delta % (quarterly) in the
industry over the 8 years covered by our study (a possibility of 32 quarters by firm instead of 16 for
Tufano). It should be noted that there are no negative net values, but that, in some quarters, certain
values exceed 100%.
(Table 1, about here)
4. RESULTS
We now move on to our estimation of the determinants. The Appendix describes the variables of the
model. Table 2 reports the distribution of the tax-save values. The values reported are in the ranges of
those of Graham and Smith for 5% reduction in volatility (Table III, page 2255). We observe that the
mean of the tax save variable is about 5% while its standard derivation is equal to 3.3%.
4As for Tufano (1996), we considered the delta-percentage as a censored variable, so we used the Tobit
model. This procedure permits us to take into account discontinuities in the dependent variable. For
example, a zero delta-percentage may mean that the firm never hedges or that the optimal hedging ratio
is nil for a given quarter. It also takes into account the fact that, below a certain optimal percentage, the
firm may decide not to hedge, so we observe a zero delta-percentage. In order to take into account of
the serial correlation of the error terms, we applied the LIMDEP panel procedure, since we had a
sufficiently large number of observations. As the results indicate in Table 3, the two sigma variables are
significant. In particular, sigma (µ) indicates that we had to make a correction for temporal correlation.
Finally, a standard test for the presence of heteroscedasticity rejects this possibility. Details will be
provided upon request.
(Tables 2 and 3, about here)
The results of the estimation using two different specifications and two different data sets are presented
in Table 3. We observe several interesting facts. The first two columns (A and B) reports coefficients
and statistical tests obtained with all the firms for which we had data, whereas the last two columns (C
and D) do so when the observations for Barrik Gold were removed. It is well documented in the
literature and in the financial press that Barrick Gold has a particular hedging strategy. For a recent
analysis see Alden (2000). Columns C and D indicate, however, that few results are sensitive to the
exclusion of Barrick Gold. To be specific, six variables remain significant at the 10% level in columns
C and D. Only the “deferred-income tax” variable is no longer significant in comparison with columns
A and B. It should be pointed out that fewer observations were used for the regressions in columns C
and D. Other specifications were estimated to verify the stability of the coefficients and their
significativity. Results are available upon request.
We now concentrate the analysis of the results on columns A and B. They indicate that seven variables
linked to maximization of the firm’s value have a significant effect (P < 0.05) on the decision to hedge.
Firms with high unit-production costs, tight financial constraint, large tax-save possibilities, and large
operations (reserves) are more likely to hedge their selling price, whereas those that defer income taxes,
pay dividends, and use preferred shares for financing will be less likely to hedge. The coefficients of the
two investment-opportunity variables are not significantly different from zero. All estimated
coefficients are consistent with the predictions, summarized in the Appendix, that link risk management
activities to the different financial variables of the firms.
The financial-distress variables are all strongly significant meaning that highly leveraged firms or those
with strong financial constraints are more likely to hedge the price of gold. Moreover, firms in good
financial health (dividend) and those using substitutes (preferred shares) for debt to finance their
investments hedge less. The other important result is related to the tax save variable. As indicated in
many theoretical studies, it is not the level of marginal tax that matters but the convexity of the tax
function. Our tax save variable is a direct measure of the convexity of the tax function. As Table 2
shows, the values of the tax save variable are very sensitive to a drop of 5% in the volatility of taxable
income. The econometric results in the four columns in Table 3 indicate clearly that a higher convex
tax function is positively correlated to the hedging strategy. This result contrasts with other studies that
found no significant relationship when using the marginal tax rate.
5Finally, our investment opportunity variables are not statistically significant. This result may indicate
that gold producers can count on a structure of positive correlation between internal financing
possibilities and investment opportunities and that this structure itself introduces a natural hedging
effect since both variables are positively correlated to the price of gold. So less financial hedging is
necessary to finance the investments.
The second column (B) indicates that if we use the “liquidity ratio” and the “firm’s long-term debt
ratio” as substitutes for the “financial-constraint” variable, the results remain unchanged. Moreover, the
coefficients for both these variables have the right sign, meaning that firms with more debt hedge more
and those with liquidity reserves have less need to hedge.
5. CONCLUSION
These preliminary results clearly indicate that many determinants related to maximization of the firm's
value are statistically significant. Particularly, variables related to tax and financial distress (or risk
premia to stakeholders) are significant. However those related to investment opportunity had no effect,
possibly because the natural hedging argument developed by Froot, Scharfstein, and Stein (1993) is
applicable in this industry.
One criticism we can make about the literature on risk management in non-financial firms is that it is
still far from adopting a portfolio approach making it possible to observe simultaneously all the firm’s
possibilities for diversification of its overall portfolio (interest rates, foreign exchange rates, commodity
prices). In other words, very few authors propose models capable of measuring potential correlation
between the different sources of risks for firms and very few approach the different strategies
simultaneously: purchase of insurance, hedging against currency exchange fluctuations, credit risk of
partners… We could extend this study by looking at all the firm’s management activities and measuring
the degree to which they are complementary and interchangeable.
REFERENCES
Alden, Edward (2000), “Brickbats Replace Bouquets for Barrick,” Financial Times, 2/10.
Caillaud, Bernard, Georges Dionne and Bruno Julien (2000), “Corporate Insurance with Optimal
Financial Contracting,” Economic Theory 16, 1, 77-105.
Cliche, Jo-Anne (2000), “Les déterminants de la gestion des risques par les entreprises non financières :
une revue de la littérature,” Assurances 67, 4, 595-636.
Doherty, Neil (2000), “Creating Value through Managing Corporate Risk: Insurance, Financial
Products and Financial Strategies,” Assurances 68, 3, 309-332.
Froot, Kenneth, David Scharfstein, and Jerimy Stein (1993), “Risk Management, Coordinating
Corporate Investment, and Financing Policies,” Journal of Finance 48, 1,629-1,658.
Graham, John and Clifford Smith (1999), “Taxes Incentives to Hedge,” Journal of Finance, December,
2,241-2,262.
MacMinn, Richard and James Garven (2000), “On Corporate Insurance,” in Handbook of Insurance,
Georges Dionne (Ed.), Kluwer Academic Publishers, 541-564.
6Nance, Deana R., Clifford Smith and Charles W. Smithson (1993), “On the Determinants of Corporate
Hedging,” Journal of Finance 48, March, 267-284.
Stulz, René (1996), “Rethinking Risk Management,” Journal of Applied Corporate Finance 9, 3, 8-24.
Tufano, Peter (1996), “Who Manages Risk? An Empirical Examination of Risk Management Practices
in the Gold Mining Industry, ” Journal of Finance, September, 1,097-1,137.
7Table 1
Distribution of Delta % Values
Value Number of observations % of observations
Delta % = 0 152 16.9
0 < Delta % < 10 % 193 21.5
10 % ≤ Delta % < 20 % 154 17.1
20 % ≤ Delta % < 30 % 127 14.1
30 % ≤ Delta % < 60 % 178 19.8
60 % ≤ Delta % < 80 % 53 5.9
80 % ≤ Delta % < 100 % 22 2.4
Delta % ≥ 100 % 19 2.1
Total 898 100
Mean 24
Standard derivation 27
8Table 2
Frequency of Tax Save (TS) and
Mean (Standard Derivation) of Explanatory Variables
Value Number of observations % of observations
TS < 0 % 27 3.01
0 ≤ TS < 1 % 0 0.00
1 ≤ TS < 2 % 12 1.34
2 ≤ TS < 3 % 165 18.37
3 ≤ TS < 4 % 97 10.80
4 ≤ TS < 5 % 153 17.04
5 ≤ TS < 6 % 140 15.59
6 ≤ TS < 7 % 74 8.24
7 ≤ TS < 8 % 101 11.25
8 ≤ TS < 9 % 78 8.69
9 ≤ TS < 10 % 32 3.56
TS ≥ 10 % 19 2.12
Total 898 100
Mean of TS 4.9
Standard derivation 3.3
9Table 3
Tobit Panel Regressions
All observations Without Barrick Gold
A B C D
Coef. P(Z>z) Coef. P(Z>z) Coef. P(Z>z) Coef. P(Z>z)
Constant –0.1771 0.003 –0.1624 0.0071 –0.2218 0.0000 –0.1986 0.0002
Tax
Deferred tax/assets –0.6314 0.0265 –0.6470 0.0382 –0.4040 0.1894 –0.4597 0.1212
Tax save 0.0118 0.0000 0.0129 0.0004 0.0089 0.0081 0.0086 0.0000
Financial distress
LT debt/MV 0.3067 0.0000 0.3687 0.0000
Unit production cost 1.0658 0.0000 1.0851 0.0000 1.2498 0.0000 1.2703 0.0000
Financial constraint 0.0936 0.0000 0.1201 0.0000
Dividend ratio –4.8635 0.0000 –4.7762 0.0000 –1.8921 0.0616 –1.8597 0.0820
Preferred shares/MV –1.2495 0.0001 –1.3903 0.0000 –0.5286 0.0701 –0.7521 0.0247
Size
Reserves 0.1080 0.0000 0.1082 0.0000 0.0361 0.0413 0.0467 0.0306
Investment opportunity
Acquisition/MV –0.0640 0.8174 –0.0329 0.9119 –0.0312 0.9112 –0.0094 0.9713
Prospecting –0.3581 0.3481 –0.4936 0.2594 0.3779 0.4417 0.1447 0.7667
expenditures/MV
Substitute
Liquidity ratio –0.5043 0.0879 –0.6878 0.0072
Statistics
Sigma (ν) 0.2023 0.0000 0.2047 0.0000 0.2012 0.0000 0.2026 0.0000
Sigma (µ) 0.1699 0.0000 0.1624 0.0000 0.1626 0.0000 0.1533 0.0000
Number of observations 898 898 868 868
Chi-square 522.32 516.33 421.53 433.79
Value p of the regression 0.0000 0.0000 0.0000 0.0000
Notes: The “unit-cost” and “reserves” variables have been divided by 1,000 and 10 respectively to fit our
software. This operation obviously does not affect the results. However, the coefficients have to be transformed to
obtain the true size of the effects. Barrick Gold is treated separately because its risk management policy differs
from that of other firms.
10Appendix
Definitions of Variables
(All dollar values are in US$)
Delta %: The Delta is the variation in the value of the portfolio of derivative products for every $1
variation in the price of gold; the aggregate value of the portfolio, calculated quarterly, is then divided by
the firm’s gold production over the same period. The Delta % measures the level of derivatives used, that
is the degree to which risks are managed.
Cash-cost: Average cash-cost to produce an ounce of gold. The variable is repeated in the four quarters
of each year, as it is only available on an annual basis. Production costs determine the firm’s
productivity; they capture the financial distress factor: when the price of gold decreases, less efficient
firms may become unable to pay current expenses. A positive sign is predicted.
Market value (MV): The number of ordinary shares multiplied by their unit market value at the end of
the quarter plus the number of preferred shares issued multiplied by their value at par plus the value of
stated liabilities.
L-T debt/MV: The book value of the long-term debt divided by the firm’s market value. Debt generates
compulsory interest payments. If the firm is unable to make its interest payments, it will get into strong
financial difficulties. This ratio therefore captures the financial distress factor. A positive sign is
predicted.
Dividend ratio: The annual per-share dividend announced, divided by the share price at the end of the
quarter. By announcing a dividend, the firm promises to pay said dividend in the future. This ratio can
capture the matching of cash flows and the future disbursements. It can also be a good indicator of the
firm’s financial health. A negative relationship is thus expected.
Preferred shares/MV: The number of preferred shares issued, multiplied by the value at par and then
divided by the firm’s market value. The preferred share is also a substitute for debt. Since an increase in
the total value of preferred shares will translate into a reduction in the ratio of indebtedness, a negative
relationship is expected.
Exploration expenditures/MV: Exploration expenditures, divided by the firm’s market value. The
variable is repeated for the four quarters of each year, as it is available only on an annual basis. The
variable captures the growth- opportunities factor to the extent that exploration efforts are profitable. A
non-significant relationship is expected, since the correlation between investment opportunities and cash
flows is positive in the gold mining industry, i.e. gold producers benefit from natural hedging.
Acquisition/MV: The value of acquisitions announced during the quarter and aimed at firms in the same
sector, divided by market value. The variable captures the growth-opportunities factor. A non-significant
relationship is presumed, since the correlation between investment opportunities and cash flow is
positive in the gold mining industry, i.e. gold producers benefit from natural hedging.
Deferred income taxes/assets: The “deferred income” item of the statement divided by total assets. Tax
credits for losses reduce deferred income, thus the ratio measures the inverse of the tax function’s
convexity. In our computations, this variable was selected because it was available on a quarterly basis,
unlike the tax-credits variable.
11Tax save: The variable was constructed with the Graham & Smith (1999) equation (1). This variable
measures the tax save expected for a 5% drop in the volatility of taxable income. The computation was
based on annual data, so the variable is repeated for the four quarters of each year. The more convex the
tax function, the greater the tax breaks. Statistics regarding this variable are reported in Table 2. A
positive sign is predicted.
Reserves: The firm’s proven and probable gold reserves (in ounces) at year’s end. The variable is
repeated for the four quarters of each year, as it is only available on an annual basis. As for the “market-
value” variable, it refers to theories related to lower average financing costs and to economies of scale
for risk-management activities. The sign is indeterminate.
Liquidity ratio: Cash on hand, short-term investments, and client accounts, divided by short-term
liabilities. Liquidities can act as a cushion for financial disasters. They are thus substitutes for risk
management and a negative relationship is expected.
Financial constraint: Binary variable. It equals 1 when the “L-T debt/MV” and “Liquidity ratio”
variables are respectively above and below the industry’s median. Otherwise it is equal to 0. A firm with
a lot of debt and a thin liquidity “cushion” is a firm operating within the limits of financial constraint.
The variable captures the financial-distress factor. A positive sign is predicted.
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